Various mechanisms exist for visual identification of roads and objects for use with autonomous vehicles. Road identification usually involves extensive mapping of the areas in which the autonomous vehicle is to travel using cameras or Lidar (Light Detection and Ranging) for creating high-definition maps. Highly Automated Vehicles (HAVs) developed now rely on detailed high-resolution maps. Changes in the environment between the time of mapping and the time of driving can cause identification errors, for instance when potholes appear or there is construction on the road. It is also unrealistic to expect to have precision maps for the entire planet. Relying on crowd-sourced updates have also failed in cases when neighborhoods purposely report inaccurate information to drive traffic away from their area.
Existing vehicular computer vision systems rely on machine learning algorithms such as convolutional neural networks (CNN) or deep neural networks (DNN) to analyze a scene and extract objects from the video frames. Additionally Radar (Radio Detection And Ranging) or Lidar are used and data from them is fused with the data extracted from images. Lidar may be prohibitively expensive for some implementations while Neural Networks may produce incorrect results or be spoofed by attackers. Machine Learning algorithms are very processing and memory demanding and adding data fusion increases rudiments for compute power presenting significant obstacles in creating autonomous or self-driving vehicles.